1. Technical Field
The present disclosure is directed machine learning, and more particularly to machine learning for tissue labeling segmentation.
2. Discussion of Related Art
Statistical based segmentation approaches for multiple organs and tissues within medical images make use of relative organ locations and intensities to define and refine the segmentation. These approaches use the creation of several tissue models based on different images features such as location and intensity levels.
An exemplary approach for automated labeling of tissues within abdominal CT scan data uses three different models to obtain label probabilities: intensity models, spatial models, and neighbor probability models. The probability models are chosen and the probability output crafted to adequately account for the probabilities from the three models. The segmentation occurs by initializing the image with the intensity probabilities and then applying iterative conditional modes (ICM) or simulated annealing methods to refine the initialization into the final segmentation. Other improved approaches may also be used, such as belief propagation. FIGS. 1A-B illustrate the creation of the probabilities from the intensity (FIG. 1A) and spatial (FIG. 1B) models for n different labels using this method.
Referring more particularly to FIGS. 1A-B, the two models (intensity and spatial) must be manually created for each label. Given the intensity value (101), it is given to the models (102 to 104) and probabilities are created (105 to 107). Similarly, given the spatial value (108), it is given to the models (109 to 111) and probabilities are created (112 to 114) with the given location. The highest intensity and spatial probabilities are taken as factors in determining the assigned label. Further manual design is necessary to determine the proper way to combine the probabilities to obtain the best segmentation. The proposed methods allow for a more scientific and automated approach to model creation and the combination of probabilities.
These approaches can be used in labeling of MR brain images with maximum likelihood estimation. A statistical approach may be used using an assumed Gibbs distribution. In another technique, spatially-variant mixture model estimates are used for pixel labeling of clinical MR brain images, wherein densities are modeled by univariate Gaussian functions.
In the above examples, models are created and combined for the observed distributions. Although the model chosen is based upon knowledge of the problem and an idea of the general distribution, no quantitative evidence is given as to suggest why a particular model is optimal for the problem. Possible over-fitting and requirements for a large among of training data appear in a histogram modeled distribution. For a parametric model such a Gaussian function the distribution may not be properly modeled under any situation. Testing other distribution or weightings for existing models can be a tedious procedure.
Therefore, a need exists for a machine learning approach, viewing the input locations, intensities, etc. as features and the distributions as classifier outputs, a more methodological approach can be taken to develop and evaluate an improved distribution model for given training datasets.